tensorflow学习3---mnist

 import tensorflow as tf
 from tensorflow.examples.tutorials.mnist import input_data

 '''数据下载'''
 mnist=input_data.read_data_sets('Mnist_data',one_hot=True)
 #one_hot标签

 '''生成层 函数'''
 def add_layer(input,in_size,out_size,n_layer='layer',activation_function=None):
     layer_name='layer %s' % n_layer
     '''补充知识'''
     #tf.name_scope:Wrapper for Graph.name_scope() using the default graph.
     #scope名字的作用域
     #sprase:A string (not ending with '/') will create a new name scope, in which name is appended to the prefix of all operations created in the context.
     #If name has been used before, it will be made unique by calling self.unique_name(name).
     with tf.name_scope('weights'):
         Weights=tf.Variable(tf.random_normal([in_size,out_size]),name='w')
         tf.summary.histogram(layer_name+'/wights',Weights)
         #tf.summary.histogram:output summary with histogram直方图
         #tf,random_normal正太分布
     with tf.name_scope('biases'):
         biases=tf.Variable(tf.zeros([1,out_size])+0.1)
         tf.summary.histogram(layer_name+'/biases',biases)
         #tf.summary.histogram:k
     with tf.name_scope('Wx_plus_b'):
         Wx_plus_b=tf.matmul(input,Weights)+biases
     if activation_function==None:
         outputs=Wx_plus_b
     else:
         outputs=activation_function(Wx_plus_b)
     tf.summary.histogram(layer_name+'/output',outputs)
     return outputs
 '''准确率'''
 def compute_accuracy(v_xs,v_ys):
     global prediction
     y_pre=sess.run(prediction,feed_dict={xs:v_xs})#<
     #tf.equal()对比预测值的索引和实际label的索引是否一样，一样返回True,否则返回false
     correct_prediction=tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1))
     #correct_prediction-->[ True False  True ...,  True  True  True]
     '''补充知识-tf.argmax'''
     #tf.argmax:Returns the index with the largest value across dimensions of a tensor.
     #tf.argmax()----->
     accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
     #正确cast为1，错误cast为0
     '''补充知识 tf.cast'''
     #tf.cast:   Casts a tensor to a new type.
     ## tensor `a` is [1.8, 2.2], dtype=tf.float
     #tf.cast(a, tf.int32) ==> [1, 2]  # dtype=tf.int32
     result=sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys})
     #print(sess.run(correct_prediction,feed_dict={xs:v_xs,ys:v_ys}))
     #ckc=tf.cast(correct_prediction,tf.float32)
     #print(sess.run(ckc,feed_dict={xs:v_xs,ys:v_ys}))
     return result

 '''占位符'''
 xs=tf.placeholder(tf.float32,[None,784])
 ys=tf.placeholder(tf.float32,[None,10])

 '''添加层'''

 prediction=add_layer(xs,784,10,activation_function=tf.nn.softmax)
 #sotmax激活函数，用于分类函数

 '''计算'''
 #交叉熵cross_entropy损失函数，参数分别为实际的预测值和实际的label值y，re
 '''补充知识'''
 #reduce_mean()
 # 'x' is [[1., 1. ]]
 #         [2., 2.]]
 #tf.reduce_mean(x) ==> 1.5
 #tf.reduce_mean(x, 0) ==> [1.5, 1.5]
 #tf.reduce_mean(x, 1) ==> [1.,  2.]
 cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1]))
 '''补充知识'''
 #reduce_sum
 # 'x' is [[1, 1, 1]]
 #         [1, 1, 1]]
 #tf.reduce_sum(x) ==> 6
 #tf.reduce_sum(x, 0) ==> [2, 2, 2]
 #tf.reduce_sum(x, 1) ==> [3, 3]
 #tf.reduce_sum(x, 1, keep_dims=True) ==> [[3], [3]]
 #tf.reduce_sum(x, [0, 1]) ==> 6

 train_step=tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

 '''Session_begin'''
 with tf.Session() as sess:
     sess.run(tf.global_variables_initializer())
     for i in range(1000):
         batch_xs,batch_ys=mnist.train.next_batch(100) #逐个batch去取数据
         sess.run(train_step,feed_dict={xs:batch_xs,ys:batch_ys})
         if(i%50==0):
             print(compute_accuracy(mnist.test.images,mnist.test.labels))